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Print ISSN: 1582-7445
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WorldCat: 643243560
doi: 10.4316/AECE


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  1/2018 - 8

 HIGHLY CITED PAPER 

An Ensemble of Classifiers based Approach for Prediction of Alzheimer's Disease using fMRI Images based on Fusion of Volumetric, Textural and Hemodynamic Features

MALIK, F. See more information about MALIK, F. on SCOPUS See more information about MALIK, F. on IEEExplore See more information about MALIK, F. on Web of Science, FARHAN, S. See more information about  FARHAN, S. on SCOPUS See more information about  FARHAN, S. on SCOPUS See more information about FARHAN, S. on Web of Science, FAHIEM, M. A. See more information about FAHIEM, M. A. on SCOPUS See more information about FAHIEM, M. A. on SCOPUS See more information about FAHIEM, M. A. on Web of Science
 
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Download PDF pdficon (1,228 KB) | Citation | Downloads: 1,138 | Views: 5,929

Author keywords
biomedical image processing, computer aided diagnosis, feature extraction, image classification, pattern recognition

References keywords
alzheimer(51), disease(38), imaging(12), functional(12), fmri(12), brain(11), diagnosis(10), dementia(8), classification(8), neuroimage(7)
Blue keywords are present in both the references section and the paper title.

About this article
Date of Publication: 2018-02-28
Volume 18, Issue 1, Year 2018, On page(s): 61 - 70
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2018.01008
Web of Science Accession Number: 000426449500008
SCOPUS ID: 85043280771

Abstract
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Alzheimer's is a neurodegenerative disease caused by the destruction and death of brain neurons resulting in memory loss, impaired thinking ability, and in certain behavioral changes. Alzheimer disease is a major cause of dementia and eventually death all around the world. Early diagnosis of the disease is crucial which can help the victims to maintain their level of independence for comparatively longer time and live a best life possible. For early detection of Alzheimer's disease, we are proposing a novel approach based on fusion of multiple types of features including hemodynamic, volumetric and textural features of the brain. Our approach uses non-invasive fMRI with ensemble of classifiers, for the classification of the normal controls and the Alzheimer patients. For performance evaluation, ten-fold cross validation is used. Individual feature sets and fusion of features have been investigated with ensemble classifiers for successful classification of Alzheimer's patients from normal controls. It is observed that fusion of features resulted in improved results for accuracy, specificity and sensitivity.


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References Weight

Web of Science® Citations for all references: 37,930 TCR
SCOPUS® Citations for all references: 0

Web of Science® Average Citations per reference: 716 ACR
SCOPUS® Average Citations per reference: 0

TCR = Total Citations for References / ACR = Average Citations per Reference

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